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Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks

Author

Listed:
  • Sonia Montecinos

    (Departamento de Física, Facultad de Ciencias, Universidad de La Serena, La Serena 1700000, Chile)

  • Carlos Rodríguez

    (Departamento de Química, Facultad de Ciencias, Universidad de La Serena, La Serena 1700000, Chile)

  • Andrea Torrejón

    (Departamento de Física, Facultad de Ciencias, Universidad de La Serena, La Serena 1700000, Chile)

  • Jorge Cortez

    (Departamento de Ingeniería de Minas, Facultad de Ingeniería, Universidad de La Serena, La Serena 1700000, Chile)

  • Marcelo Jaque

    (Instituto Multidisciplinario de Investigación y Postgrado, Universidad de La Serena, La Serena 1700000, Chile)

Abstract

The performance of photovoltaic solar panels is influenced by their temperature, so there is a need for a tool that can accurately and instantly predict the panel temperature. This paper presents an analysis of the panel temperature’s dependence on atmospheric parameters at an operational photovoltaic park in the semi-arid north of Chile using Artificial Neural Networks (ANNs). We applied the back-propagation algorithm to train the model by using the atmospheric variables tilted solar radiation (TSR), air temperature, and wind speed measured in the park. The ANN model’s effectiveness was evaluated by comparing it to five different deterministic models: the Standard model, King’s model, Faiman’s model, Mattei’s model, and Skoplaki’s model. Additionally, we examined the sensitivity of panel temperature to changes in air temperature, TSR, and wind speed. Our findings show that the ANN model had the best prediction accuracy for panel temperature, with a Root Mean Squared Error (RMSE) of 1.59 °C, followed by Mattei’s model with a higher RMSE of 3.30 °C. We also determined that air temperature has the most significant impact on panel temperature, followed by TSR and wind speed. These results demonstrate that the ANN is a powerful tool for predicting panel temperature with high accuracy.

Suggested Citation

  • Sonia Montecinos & Carlos Rodríguez & Andrea Torrejón & Jorge Cortez & Marcelo Jaque, 2024. "Panel Temperature Dependence on Atmospheric Parameters of an Operative Photovoltaic Park in Semi-Arid Zones Using Artificial Neural Networks," Energies, MDPI, vol. 17(23), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:5844-:d:1526625
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    References listed on IDEAS

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    2. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    3. Ma, Tao & Yang, Hongxing & Lu, Lin, 2017. "Long term performance analysis of a standalone photovoltaic system under real conditions," Applied Energy, Elsevier, vol. 201(C), pages 320-331.
    4. Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
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